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EUROPAR
1999
Springer
14 years 1 months ago
Parallel k/h-Means Clustering for Large Data Sets
This paper describes the realization of a parallel version of the k/h-means clustering algorithm. This is one of the basic algorithms used in a wide range of data mining tasks. We ...
Kilian Stoffel, Abdelkader Belkoniene
UIC
2009
Springer
14 years 3 months ago
Mining and Visualizing Mobile Social Network Based on Bayesian Probabilistic Model
Social networking has provided powerful new ways to find people, organize groups, and share information. Recently, the potential functionalities of the ubiquitous infrastructure le...
Jun-Ki Min, Su-Hyung Jang, Sung-Bae Cho
MSR
2005
ACM
14 years 2 months ago
Mining evolution data of a product family
Diversiļ¬cation of software assets through changing requirements impose a constant challenge on the developers and maintainers of large software systems. Recent research has addr...
Michael Fischer, Johann Oberleitner, Jacek Ratzing...
FIMI
2003
170views Data Mining» more  FIMI 2003»
13 years 10 months ago
kDCI: a Multi-Strategy Algorithm for Mining Frequent Sets
This paper presents the implementation of kDCI, an enhancement of DCI [10], a scalable algorithm for discovering frequent sets in large databases. The main contribution of kDCI re...
Salvatore Orlando, Claudio Lucchese, Paolo Palmeri...
ICTAI
2007
IEEE
14 years 3 months ago
Dragon Toolkit: Incorporating Auto-Learned Semantic Knowledge into Large-Scale Text Retrieval and Mining
The majority of text retrieval and mining techniques are still based on exact feature (e.g. words) matching and unable to incorporate text semantics. Many researchers believe that...
Xiaohua Zhou, Xiaodan Zhang, Xiaohua Hu